We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used; we deployed the...

Much research has examined the drivers of firms' R&D investments. However, many questions remain with respect to the role of R&D as a learning target and as a means of achieving legitimacy, particularly in the context of imitative R&D strategy. We develop a theory that integrates different...

Using a novel and unique panel dataset of individual-level professional forecasts at short, medium, and very-long horizons, we provide new stylized facts about survey forecasts. We present direct evidence that forecasters use multivariate models in an environment with imperfect information about...

The natural interest rate is the real rate that would prevail in the long-run. The standard view in macroeconomics is that the natural rate depends exclusively on structural factors such as productivity growth and demographics. This paper challenges this view by discussing three alternative, and...

This paper provides an overview of behaviors and learning of heterogeneous economic agents -whether individuals or firms- within complex evolving economies. It aims to explore the microfoundation of agency and learning, two fundamental aspects that constitutively contribute to the emergence of...